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<pre><code class="r">##############################################################
##############################################################
### Replication Materials for
### Stefan Müller and Liam Kneafsey:
### Evidence for the Irrelevance of Irrelevant Events
### Political Science Research and Methods
###
### Please get in touch with the authors if you have any questions: 
### stefan.mueller@ucd.ie
### Note: 0000_readme.pdf contains 
### instructions and details about each script and dataset
##############################################################
##############################################################

## load packages

library(haven)   # CRAN v2.3.1
library(ggplot2) # CRAN v3.3.3
library(dplyr)   # CRAN v1.0.5
library(car)     # CRAN v3.0-10
library(scales)  # CRAN v1.1.1
library(forcats) # CRAN v0.5.1
library(tidyr)   # CRAN v1.1.3


## show how we subsetted the 2014 EES raw data which you can download after 
## a free registration at: https://doi.org/10.4232/1.12628
## dat_raw &lt;- haven::read_dta(&quot;data/ZA5160_v4-0-0.dta&quot;)
## 
## dat &lt;- select(dat_raw, starts_with(&quot;qpp23_&quot;),
##               countrycode)
## saveRDS(dat, &quot;data/data_ess_2014_knowledge.rds&quot;)

dat &lt;- readRDS(&quot;data_ess_2014_knowledge.rds&quot;)

source(&quot;function_theme_base.R&quot;)

## recode knowledge variables by coding &quot;correct&quot; and incorrect/missing answers
dat_knowledge &lt;- dat %&gt;% 
    mutate(know_ch = car::recode(qpp23_1, &quot;-9=0;1=1;2=0&quot;),
           know_ep = car::recode(qpp23_2, &quot;-9=0;1=1;2=0&quot;),
           know_natparl = car::recode(qpp23_3, &quot;-9=0;1=1;2=0&quot;),
           know_pm = car::recode(qpp23_4, &quot;-9=0;1=1;2=0&quot;)) %&gt;% 
    mutate(know_scale = know_ch +  know_ep + know_natparl +
               know_pm, na.rm = TRUE)


recode_countries &lt;- c(&#39;&quot;1040&quot;=&quot;Austria&quot;;
&quot;1056&quot;=&quot;Belgium&quot;;
&quot;1100&quot;=&quot;Bulgaria&quot;;
&quot;1191&quot;=&quot;Croatia&quot;;
&quot;1196&quot;=&quot;Cyprus&quot;;
&quot;1203&quot;=&quot;Czech Republic&quot;;
&quot;1208&quot;=&quot;Denmark&quot;;
&quot;1233&quot;=&quot;Estonia&quot;;
&quot;1246&quot;=&quot;Finland&quot;;
&quot;1250&quot;=&quot;France&quot;;
&quot;1276&quot;=&quot;Germany&quot;;
&quot;1300&quot;=&quot;Greece&quot;;
&quot;1348&quot;=&quot;Hungary&quot;;
&quot;1372&quot;=&quot;Ireland&quot;;
&quot;1380&quot;=&quot;Italy&quot;;
&quot;1428&quot;=&quot;Latvia&quot;;
&quot;1440&quot;=&quot;Lithuania&quot;;
&quot;1442&quot;=&quot;Luxembourg&quot;;
&quot;1470&quot;=&quot;Malta&quot;;
&quot;1528&quot;=&quot;The Netherlands&quot;;
&quot;1616&quot;=&quot;Poland&quot;;
&quot;1620&quot;=&quot;Portugal&quot;;
&quot;1642&quot;=&quot;Romania&quot;;
&quot;1703&quot;=&quot;Slovakia&quot;;
&quot;1705&quot;=&quot;Slovenia&quot;;
&quot;1724&quot;=&quot;Spain&quot;;
&quot;1752&quot;=&quot;Sweden&quot;;
&quot;1826&quot;=&quot;United Kingdom&quot;&#39;)

dat_knowledge &lt;- dat_knowledge %&gt;% 
    mutate(country = car::recode(as.character(countrycode), 
                                 recode_countries))


nrow(dat_knowledge)
</code></pre>

<pre><code>## [1] 30064
</code></pre>

<pre><code class="r">length(unique(dat_knowledge$country))
</code></pre>

<pre><code>## [1] 28
</code></pre>

<pre><code class="r">## get respondents by country
n_respondents &lt;- dat_knowledge %&gt;% 
    group_by(country) %&gt;% 
    count() %&gt;% 
    ungroup() %&gt;% 
    arrange(-n)

mean(n_respondents$n)
</code></pre>

<pre><code>## [1] 1073.714
</code></pre>

<pre><code class="r">## convert to long format and identify Irish respondents
dat_knowledge_long &lt;- dat_knowledge %&gt;% 
    select(know_ch:know_pm, country) %&gt;% 
    gather(question, value, -country) %&gt;% 
    mutate(ireland_dummy = ifelse(country == &quot;Ireland&quot;, &quot;Ireland&quot;, &quot;Other EU member states&quot;)) 
</code></pre>

<pre><code>## Warning: attributes are not identical across measure variables;
## they will be dropped
</code></pre>

<pre><code class="r">## get averages for Irish respondents/all other respondents and confidence intervals
dat_knowledge_questions &lt;- dat_knowledge_long %&gt;%   
    group_by(question, ireland_dummy) %&gt;% 
    summarise(mean = mean(value),
              se = sd(value) / sqrt(n()),
              ci_lower_95 = mean - 1.96 * se,
              ci_upper_95 = mean + 1.96 * se,
              ci_lower_90 = mean - 1.645 * se,
              ci_upper_90 = mean + 1.645 * se)
</code></pre>

<pre><code>## `summarise()` has grouped output by &#39;question&#39;. You can override using the `.groups` argument.
</code></pre>

<pre><code class="r">table(dat_knowledge_questions$question)
</code></pre>

<pre><code>## 
##      know_ch      know_ep 
##            2            2 
## know_natparl      know_pm 
##            2            2
</code></pre>

<pre><code class="r">recode_questions &lt;- c(&quot;
                      &#39;know_ch&#39;=&#39;Switzerland member of EU&#39;;
                      &#39;know_ep&#39;=&#39;Allocation of seats in the European Parliament&#39;;
                      &#39;know_natparl&#39;=&#39;Size of national legislature&#39;;
                      &#39;know_pm&#39;=&#39;Party of current Prime Minister&#39;&quot;)


dat_knowledge_questions &lt;- dat_knowledge_questions %&gt;% 
    mutate(question_detail = car::recode(question, recode_questions)) %&gt;% 
    mutate(percentage_print = paste0(round(mean * 100, 0), &quot;%&quot;)) %&gt;% 
    mutate(question_type = ifelse(question %in% c(&quot;know_ch&quot;, &quot;know_ep&quot;),
                                  &quot;EU politics:&quot;, &quot;Domestic politics:&quot;))


dat_knowledge_questions$question_type &lt;- factor(dat_knowledge_questions$question_type,
                                                levels = c(&quot;EU politics:&quot;, &quot;Domestic politics:&quot;))
ggplot(dat_knowledge_questions, 
       aes(x = forcats::fct_rev(ireland_dummy), y = mean,
           colour = ireland_dummy)) +
    geom_point() +
    geom_linerange(aes(ymin = ci_lower_95,
                       ymax = ci_upper_95),
                   size = 0.5) +
    geom_text(aes(label = percentage_print),
              nudge_x = 0.3) +
    geom_linerange(aes(ymin = ci_lower_90,
                       ymax = ci_upper_90),
                   size = 1.3) +
    scale_colour_manual(values = c(&quot;black&quot;, &quot;grey50&quot;)) +
    coord_flip() +
    facet_wrap(question_type~question_detail, nrow = 4,
               scales = &quot;free_x&quot;) +
    scale_y_continuous(limits = c(0, 1),
                       labels = scales::percent_format(accuracy = 1)) +
    labs(y = &quot;Correct answers to question&quot;, 
         x = NULL) +
    theme(legend.position = &quot;none&quot;)
</code></pre>

<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">ggsave(&quot;fig_a31.pdf&quot;, 
       width = 9, height = 8)



table(dat_knowledge_long$question,
      dat_knowledge_long$country)
</code></pre>

<pre><code>##               
##                Austria
##   know_ch         1114
##   know_ep         1114
##   know_natparl    1114
##   know_pm         1114
##               
##                Belgium
##   know_ch         1084
##   know_ep         1084
##   know_natparl    1084
##   know_pm         1084
##               
##                Bulgaria
##   know_ch          1123
##   know_ep          1123
##   know_natparl     1123
##   know_pm          1123
##               
##                Croatia Cyprus
##   know_ch         1078    530
##   know_ep         1078    530
##   know_natparl    1078    530
##   know_pm         1078    530
##               
##                Czech Republic
##   know_ch                1177
##   know_ep                1177
##   know_natparl           1177
##   know_pm                1177
##               
##                Denmark
##   know_ch         1085
##   know_ep         1085
##   know_natparl    1085
##   know_pm         1085
##               
##                Estonia
##   know_ch         1087
##   know_ep         1087
##   know_natparl    1087
##   know_pm         1087
##               
##                Finland France
##   know_ch         1096   1074
##   know_ep         1096   1074
##   know_natparl    1096   1074
##   know_pm         1096   1074
##               
##                Germany Greece
##   know_ch         1648   1085
##   know_ep         1648   1085
##   know_natparl    1648   1085
##   know_pm         1648   1085
##               
##                Hungary
##   know_ch         1104
##   know_ep         1104
##   know_natparl    1104
##   know_pm         1104
##               
##                Ireland Italy
##   know_ch         1081  1091
##   know_ep         1081  1091
##   know_natparl    1081  1091
##   know_pm         1081  1091
##               
##                Latvia
##   know_ch        1055
##   know_ep        1055
##   know_natparl   1055
##   know_pm        1055
##               
##                Lithuania
##   know_ch           1096
##   know_ep           1096
##   know_natparl      1096
##   know_pm           1096
##               
##                Luxembourg
##   know_ch             538
##   know_ep             538
##   know_natparl        538
##   know_pm             538
##               
##                Malta Poland
##   know_ch        544   1223
##   know_ep        544   1223
##   know_natparl   544   1223
##   know_pm        544   1223
##               
##                Portugal
##   know_ch          1033
##   know_ep          1033
##   know_natparl     1033
##   know_pm          1033
##               
##                Romania
##   know_ch         1108
##   know_ep         1108
##   know_natparl    1108
##   know_pm         1108
##               
##                Slovakia
##   know_ch          1095
##   know_ep          1095
##   know_natparl     1095
##   know_pm          1095
##               
##                Slovenia Spain
##   know_ch          1143  1106
##   know_ep          1143  1106
##   know_natparl     1143  1106
##   know_pm          1143  1106
##               
##                Sweden
##   know_ch        1144
##   know_ep        1144
##   know_natparl   1144
##   know_pm        1144
##               
##                The Netherlands
##   know_ch                 1101
##   know_ep                 1101
##   know_natparl            1101
##   know_pm                 1101
##               
##                United Kingdom
##   know_ch                1421
##   know_ep                1421
##   know_natparl           1421
##   know_pm                1421
</code></pre>

<pre><code class="r">table(dat_knowledge$know_scale,
      dat_knowledge$country)
</code></pre>

<pre><code>##    
##     Austria Belgium Bulgaria
##   0      57      38      647
##   1     629     621      330
##   2     303     344      117
##   3     110      73       24
##   4      15       8        5
##    
##     Croatia Cyprus
##   0      34     49
##   1     699    349
##   2     263    102
##   3      74     28
##   4       8      2
##    
##     Czech Republic Denmark
##   0            124      11
##   1            652     746
##   2            270     250
##   3            108      73
##   4             23       5
##    
##     Estonia Finland France
##   0     151      42    151
##   1     698     740    613
##   2     196     244    257
##   3      30      52     48
##   4      12      18      5
##    
##     Germany Greece Hungary
##   0      19     28      31
##   1    1147    611     775
##   2     367    333     220
##   3      95     94      59
##   4      20     19      19
##    
##     Ireland Italy Latvia
##   0      86    95    274
##   1     494   479    583
##   2     339   344    147
##   3     132   138     42
##   4      30    35      9
##    
##     Lithuania Luxembourg
##   0       112         76
##   1       727        375
##   2       198         72
##   3        49         14
##   4        10          1
##    
##     Malta Poland Portugal
##   0    10     61       49
##   1   387    773      458
##   2   116    280      363
##   3    28     84      133
##   4     3     25       30
##    
##     Romania Slovakia Slovenia
##   0     110       33      166
##   1     506      621      785
##   2     303      314      164
##   3     133       85       26
##   4      56       42        2
##    
##     Spain Sweden
##   0    21      8
##   1   635    831
##   2   319    247
##   3   105     52
##   4    26      6
##    
##     The Netherlands
##   0              44
##   1             785
##   2             226
##   3              42
##   4               4
##    
##     United Kingdom
##   0            104
##   1            779
##   2            400
##   3            121
##   4             17
</code></pre>

<pre><code class="r">## calculate average of corrrectly answered questions by country

dat_knowledge_scale_sum &lt;- dat_knowledge %&gt;% 
    group_by(country) %&gt;% 
    summarise(mean = mean(know_scale),
              se = sd(know_scale) / sqrt(n()),
              ci_lower_95 = mean - 1.96 * se,
              ci_upper_95 = mean + 1.96 * se,
              ci_lower_90 = mean - 1.645 * se,
              ci_upper_90 = mean + 1.645 * se)


dat_knowledge_scale_sum &lt;- dat_knowledge_scale_sum %&gt;% 
    mutate(ireland_dummy = ifelse(country == &quot;Ireland&quot;, TRUE, FALSE))


ggplot(dat_knowledge_scale_sum, 
       aes(x = reorder(country, mean), y = mean,
           colour = ireland_dummy)) +
    geom_point() +
    geom_text(aes(label = country,
                  y = mean),
              hjust = 1, nudge_y = -0.07) +
    geom_linerange(aes(ymin = ci_lower_95,
                       ymax = ci_upper_95),
                   size = 0.5) +
    geom_linerange(aes(ymin = ci_lower_90,
                       ymax = ci_upper_90),
                   size = 1.3) +
    scale_colour_manual(values = c(&quot;grey50&quot;, &quot;black&quot;)) +
    coord_flip() +
    scale_y_continuous(limits = c(0, 2),
                       breaks = c(seq(0, 2, 0.5))) +
    labs(y = &quot;Number of questions answered correctly (out of 4)&quot;, 
         x = NULL) +
    theme(legend.position = &quot;none&quot;,
          axis.text.y = element_blank(),
          axis.ticks.y = element_blank())
</code></pre>

<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">ggsave(&quot;fig_a30.pdf&quot;, 
       width = 9, height = 6)



## download RAW INES data at https://www.ucd.ie/issda/data/irishnationalelectionstudy/
## Here, we selected only the relevant variables, 
## and saved the subsetted dataset
## ines_raw &lt;- foreign::read.dta(&quot;data_raw/INESLong_Beta.dta&quot;)
## ines_subset &lt;- select(ines_raw, id,
##                       c(v0001, v0870,
##                         v0871, v0872,
##                         v0873, ines))
## saveRDS(ines_subset, &quot;data_ines_subset.rds&quot;)

## load dataset
ines_subset &lt;- readRDS(&quot;data_ines_subset.rds&quot;)

## for election in 2002 check whether respondent contacted politician
ines_2002 &lt;- ines_subset %&gt;% 
    mutate(year = ines,
           contact = v0870) %&gt;% 
    filter(year == 2002)

table(ines_2002$v0870)
</code></pre>

<pre><code>## 
##       yes        no dont know 
##       802      1822        27 
##  dontknow 
##         0
</code></pre>

<pre><code class="r">ines_2002 &lt;- ines_2002 %&gt;% 
    mutate(contact_td = case_when(
        v0871 == &quot;td&quot; ~ 1,
        v0872 == &quot;td&quot; ~ 1,
        v0873 == &quot;td&quot; ~ 1
    ))


ines_2002 &lt;- ines_2002 %&gt;% 
    mutate(contact_councillor = case_when(
        v0871 == &quot;councillors&quot; ~ 1,
        v0872 == &quot;councillors&quot; ~ 1,
        v0873 == &quot;councillors&quot; ~ 1
    ))

ines_2002_contact &lt;- ines_2002 %&gt;% 
    select(starts_with(&quot;contact_&quot;)) %&gt;% 
    gather(type, yes) %&gt;% 
    mutate(yes = ifelse(is.na(yes), 0, yes)) %&gt;% 
    mutate(type_plot = str_remove_all(type, &quot;contact_&quot;)) %&gt;% 
    mutate(type_plot = car::recode(type_plot, &quot;&#39;td&#39;=&#39;TD&#39;;
                                 &#39;councillor&#39;=&#39;Local councilor&#39;&quot;)) 

## mean and 95 per cent CIs

dat_sum_contact &lt;- ines_2002_contact %&gt;%
    group_by(type_plot) %&gt;%
    summarise(mean = mean(yes),
              se = sd(yes) / sqrt(n()),
              ci_lower_95 = mean - 1.96 * se,
              ci_upper_95 = mean + 1.96 * se,
              ci_lower_90 = mean - 1.645 * se,
              ci_upper_90 = mean + 1.645 * se)


ggplot(data = dat_sum_contact,
       aes(x = type_plot,
           y = mean)) +
    geom_point() +
    geom_linerange(aes(ymin = ci_lower_95, ymax = ci_upper_95)) +
    geom_linerange(aes(ymin = ci_lower_90, ymax = ci_upper_90),
                   size = 1.3) +
    scale_y_continuous(labels = scales::percent_format(accuracy = 1),
                       limits = c(0, 0.3)) +
    coord_flip() + 
    geom_text(aes(label = paste0(100 * round(mean,2), &quot;%&quot;)),
              nudge_x = 0.3, vjust = 0.5) +
    labs(x = NULL,
         y = &quot;Percentage of respondents who contacted politician&quot;) +
    guides(colour = guide_legend(reverse = TRUE),
           shape = guide_legend(reverse = TRUE)) 
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">ggsave(&quot;fig_a32.pdf&quot;, 
       width = 9, height = 2.5)


## now check whether one or more politicians called the respondent&#39;s home
ines_candidate_called &lt;- ines_subset %&gt;% 
    filter(ines %in% c(2002, 2007)) %&gt;% 
    select(candidate_called_home = v0001, ines, id) %&gt;% 
    filter(!is.na(candidate_called_home)) %&gt;% 
    mutate(candidate_called_home = ifelse(candidate_called_home == &quot;yes&quot;, 1, 0))


dat_sum_candidate_called &lt;- ines_candidate_called %&gt;%
    group_by(ines) %&gt;%
    summarise(mean = mean(candidate_called_home),
              se = sd(candidate_called_home) / sqrt(n()),
              ci_lower_95 = mean - 1.96 * se,
              ci_upper_95 = mean + 1.96 * se,
              ci_lower_90 = mean - 1.645 * se,
              ci_upper_90 = mean + 1.645 * se)


dat_sum_candidate_called$ines &lt;- factor(dat_sum_candidate_called$ines,
                                        levels = c(&quot;2007&quot;, &quot;2002&quot;))

## Figure A33 ----
ggplot(data = dat_sum_candidate_called,
       aes(x = factor(ines),
           y = mean)) +
    geom_point() +
    geom_linerange(aes(ymin = ci_lower_95, ymax = ci_upper_95)) +
    geom_linerange(aes(ymin = ci_lower_90, ymax = ci_upper_90),
                   size = 1.3) +
    scale_y_continuous(labels = scales::percent_format(accuracy = 1),
                       limits = c(0, 1)) +
    coord_flip() +
    geom_text(aes(label = paste0(100 * round(mean,2), &quot;%&quot;)),
              nudge_x = 0.3, vjust = 0.5) +
    labs(x = NULL,
         y = &quot;At least one candidate called to respondent&#39;s home&quot;) +
    guides(colour = guide_legend(reverse = TRUE),
           shape = guide_legend(reverse = TRUE)) 
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">ggsave(&quot;fig_a33.pdf&quot;, 
       width = 9, height = 2.5)

## script executed successfully on
date()
</code></pre>

<pre><code>## [1] &quot;Wed Jun 23 10:02:59 2021&quot;
</code></pre>

<pre><code class="r">sessionInfo()
</code></pre>

<pre><code>## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS  10.16
## 
## Matrix products: default
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_IE.UTF-8/en_IE.UTF-8/en_IE.UTF-8/C/en_IE.UTF-8/en_IE.UTF-8
## 
## attached base packages:
## [1] grid      stats    
## [3] graphics  grDevices
## [5] utils     datasets 
## [7] methods   base     
## 
## other attached packages:
##  [1] forcats_0.5.1     
##  [2] haven_2.3.1       
##  [3] scales_1.1.1      
##  [4] Hmisc_4.4-2       
##  [5] Formula_1.2-4     
##  [6] lattice_0.20-41   
##  [7] parameters_0.13.0 
##  [8] MuMIn_1.43.17     
##  [9] ebal_0.1-6        
## [10] jtools_2.1.2      
## [11] lubridate_1.7.10  
## [12] broom.mixed_0.2.6 
## [13] broom_0.7.4       
## [14] lme4_1.1-26       
## [15] survey_4.0        
## [16] survival_3.2-7    
## [17] Matrix_1.3-2      
## [18] WeightIt_0.11.0   
## [19] cobalt_4.2.4      
## [20] car_3.0-10        
## [21] carData_3.0-4     
## [22] cowplot_1.1.1     
## [23] here_1.0.1        
## [24] modelsummary_0.8.0
## [25] ggeffects_1.0.1   
## [26] estimatr_0.30.2   
## [27] ggplot2_3.3.3     
## [28] stringr_1.4.0     
## [29] tidyr_1.1.3       
## [30] dplyr_1.0.5       
## [31] knitr_1.31        
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.3.1       
##   [2] uuid_0.1-4         
##   [3] backports_1.2.1    
##   [4] systemfonts_1.0.1  
##   [5] plyr_1.8.6         
##   [6] TMB_1.7.19         
##   [7] splines_4.0.2      
##   [8] TH.data_1.0-10     
##   [9] digest_0.6.27      
##  [10] htmltools_0.5.1.1  
##  [11] fansi_0.4.2        
##  [12] magrittr_2.0.1     
##  [13] checkmate_2.0.0    
##  [14] cluster_2.1.0      
##  [15] openxlsx_4.2.3     
##  [16] officer_0.3.16     
##  [17] sandwich_3.0-0     
##  [18] jpeg_0.1-8.1       
##  [19] colorspace_2.0-0   
##  [20] rvest_0.3.6        
##  [21] ggrepel_0.9.1      
##  [22] mitools_2.4        
##  [23] xfun_0.20          
##  [24] crayon_1.4.1       
##  [25] zoo_1.8-8          
##  [26] glue_1.4.2         
##  [27] kableExtra_1.3.1   
##  [28] gtable_0.3.0       
##  [29] emmeans_1.5.4      
##  [30] webshot_0.5.2      
##  [31] abind_1.4-5        
##  [32] annotater_0.1.3    
##  [33] mvtnorm_1.1-1      
##  [34] DBI_1.1.1          
##  [35] Rcpp_1.0.6         
##  [36] viridisLite_0.4.0  
##  [37] xtable_1.8-4       
##  [38] htmlTable_2.1.0    
##  [39] foreign_0.8-81     
##  [40] stats4_4.0.2       
##  [41] htmlwidgets_1.5.3  
##  [42] httr_1.4.2         
##  [43] RColorBrewer_1.1-2 
##  [44] ellipsis_0.3.2     
##  [45] pkgconfig_2.0.3    
##  [46] farver_2.1.0       
##  [47] nnet_7.3-15        
##  [48] utf8_1.2.1         
##  [49] tidyselect_1.1.1   
##  [50] labeling_0.4.2     
##  [51] rlang_0.4.11       
##  [52] reshape2_1.4.4     
##  [53] munsell_0.5.0      
##  [54] cellranger_1.1.0   
##  [55] tools_4.0.2        
##  [56] cli_2.5.0          
##  [57] generics_0.1.0     
##  [58] sjlabelled_1.1.7   
##  [59] evaluate_0.14      
##  [60] tables_0.9.6       
##  [61] zip_2.1.1          
##  [62] pander_0.6.3       
##  [63] purrr_0.3.4        
##  [64] nlme_3.1-152       
##  [65] mime_0.10          
##  [66] xml2_1.3.2         
##  [67] compiler_4.0.2     
##  [68] rstudioapi_0.13    
##  [69] curl_4.3           
##  [70] png_0.1-7          
##  [71] tibble_3.1.1       
##  [72] statmod_1.4.35     
##  [73] stringi_1.5.3      
##  [74] highr_0.8          
##  [75] gdtools_0.2.3      
##  [76] texreg_1.37.5      
##  [77] nloptr_1.2.2.2     
##  [78] markdown_1.1       
##  [79] vctrs_0.3.8        
##  [80] pillar_1.6.0       
##  [81] lifecycle_1.0.0    
##  [82] estimability_1.3   
##  [83] data.table_1.14.0  
##  [84] insight_0.14.0     
##  [85] flextable_0.6.3    
##  [86] R6_2.5.0           
##  [87] latticeExtra_0.6-29
##  [88] gridExtra_2.3      
##  [89] rio_0.5.16         
##  [90] codetools_0.2-18   
##  [91] boot_1.3-27        
##  [92] MASS_7.3-53        
##  [93] assertthat_0.2.1   
##  [94] rprojroot_2.0.2    
##  [95] withr_2.4.2        
##  [96] multcomp_1.4-16    
##  [97] mgcv_1.8-33        
##  [98] bayestestR_0.8.2   
##  [99] hms_1.0.0          
## [100] rpart_4.1-15       
## [101] coda_0.19-4        
## [102] minqa_1.2.4        
## [103] rmarkdown_2.6      
## [104] snakecase_0.11.0   
## [105] base64enc_0.1-3
</code></pre>

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